title: Vectorized algorithms for spiking neural network simulation.
abstract: High-level languages (Matlab, Python) are popular in neuroscience because they are flexible and accelerate development. However, for simulating spiking neural networks, the cost of interpretation is a bottleneck. We describe a set of algorithms to simulate large spiking neural networks efficiently with high-level languages using vector-based operations. These algorithms constitute the core of Brian, a spiking neural network simulator written in the Python language. Vectorized simulation makes it possible to combine the flexibility of high-level languages with the computational efficiency usually associated with compiled languages.
authors.affiliation: Laboratoire Psychologie de la Perception, CNRS and Université Paris Descartes, Paris 75006, France, and Département d'Etudes Cognitives, Ecole Normale Supérieure, Paris Cedex 05, 75230 France. romain.brette@ens.fr
authors.forename: Romain
authors.initials: R
authors.lastname: Brette
authors.forename: Dan F M
authors.initials: DF
authors.lastname: Goodman
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D003198:Computer Simulation; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D013569:Synapses / Q000502:physiology